I assume in the short term, one of the most interesting application of 
(Chat)GPT for sage might be the "Copilot for docs" project from github: 
https://githubnext.com/projects/copilot-for-docs/ They train the model on 
the official docs (/ source code?) and thus are able to provide better 
results. This should take care of the above-mentioned issues that the 
generated code is not for sage but for scipy/mathematica/...

On Saturday, April 22, 2023 at 4:29:34 AM UTC+8 Oscar Benjamin wrote:

> On Fri, 21 Apr 2023 at 20:36, William Stein <wst...@gmail.com> wrote:
> >
> > There's is a discussion right now on HN about LLM's trained on code
> >
> > https://news.ycombinator.com/item?id=35657982
> >
> > One of the comments https://news.ycombinator.com/item?id=35658118
> > points out that most of the non-GPL super permissive licenses require
> > explicit attribution when creating derived works. If the output of an
> > LLM is a derived work (and not just some fair use of that input), then
> > there is legally nothing particularly special about GPL in the context
> > of training LLM's. That I think successfully undercuts my point in
> > starting this thread.
>
> I don't think GPL or other licenses really matter here. It won't be
> long before these models can produce code that is sufficiently
> original/distinct that it would not be considered "derived" anyway.
> The fact that the model happened to learn in part from looking at lots
> of code with different licenses is not really that different from the
> way that humans learn programming. If I happen to look at the Sage
> source code and learn something in the process it does not mean that
> Sage's GPL conditions apply to all future code I write after gaining
> that knowledge. There is a spectrum from using knowledge learned from
> code through to adapting the code and in the extreme just copying the
> code. Pretty soon these models will be able to position themselves
> wherever you want on that spectrum.
>
> --
> Oscar
>

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